Sift Case Studies How Favor Delivery achieved growth while reducing risk

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	How Favor Delivery achieved growth while reducing risk Sift

How Favor Delivery achieved growth while reducing risk

Sift

Technology Category	Application Infrastructure & Middleware - API Integration & Management
Applicable Industries	Retail
Applicable Functions	Sales & Marketing
Use Cases	Fraud Detection
Services	System Integration
Challenge	As Favor Delivery expanded, they experienced an increase in the number of chargebacks. The growth of fraudulent accounts and account takeover (ATO) attempts were becoming more frequent. Favor Delivery was using their internal heuristic system to manually search for fraud, which wasn’t scalable and couldn’t keep up with the volume of incoming orders. They needed a proactive solution that could automate and keep them ahead of fraud – not struggling to keep up with it. Read More
About Customer	Favor Delivery is a Texas-based on-demand delivery service. Via the mobile app or desktop site, users can place orders for anything from takeout food to last-minute needs from the drugstore, and Runners (delivery assistants) make the delivery in under an hour while keeping users updated every step of the way. Users get what they need in good time and at a great price, while Runners have the freedom to either supplement their income or replace a traditional job by delivering with Favor Delivery. Favor Delivery operates within all major cities in Texas and is continuing to expand throughout the state. Read More
Solution	Favor Delivery turned to Sift Payment Protection; the integration took less than two months, and within a month after integration Favor Delivery started to see powerful results. Favor Delivery’s Account Review Team began utilizing Sift Workflows to manage their fraud logic, auto-accepting most orders and auto-blocking the riskiest. With Sift Insights, the team used the Routes metrics to determine how many orders were hitting a given route and whether that was effective or causing too many false positives. And the Network and Activity features within an order accelerated manual reviews, as the team could determine whether an order shared risky attributes with other fraudulent orders and quickly decide whether they wanted to accept or reject the order. Read More Log in to view content
Contents

Technology Category

Application Infrastructure & Middleware - API Integration & Management

Applicable Industries

Retail

Applicable Functions

Sales & Marketing

Use Cases

Fraud Detection

Services

System Integration

Challenge

As Favor Delivery expanded, they experienced an increase in the number of chargebacks. The growth of fraudulent accounts and account takeover (ATO) attempts were becoming more frequent. Favor Delivery was using their internal heuristic system to manually search for fraud, which wasn’t scalable and couldn’t keep up with the volume of incoming orders. They needed a proactive solution that could automate and keep them ahead of fraud – not struggling to keep up with it.

About Customer

Favor Delivery is a Texas-based on-demand delivery service. Via the mobile app or desktop site, users can place orders for anything from takeout food to last-minute needs from the drugstore, and Runners (delivery assistants) make the delivery in under an hour while keeping users updated every step of the way. Users get what they need in good time and at a great price, while Runners have the freedom to either supplement their income or replace a traditional job by delivering with Favor Delivery. Favor Delivery operates within all major cities in Texas and is continuing to expand throughout the state.

Solution

Favor Delivery turned to Sift Payment Protection; the integration took less than two months, and within a month after integration Favor Delivery started to see powerful results. Favor Delivery’s Account Review Team began utilizing Sift Workflows to manage their fraud logic, auto-accepting most orders and auto-blocking the riskiest. With Sift Insights, the team used the Routes metrics to determine how many orders were hitting a given route and whether that was effective or causing too many false positives. And the Network and Activity features within an order accelerated manual reviews, as the team could determine whether an order shared risky attributes with other fraudulent orders and quickly decide whether they wanted to accept or reject the order.

Impact #1	Since implementing Sift Payment Protection, Favor Delivery’s chargeback rate has decreased significantly.
Impact #2	Thanks to accurate, automated decisions, the Account Review team is saving significant time in manual review.
Impact #3	With their chargeback rate under control, Favor Delivery has scaled throughout the state of Texas and continues to grow steadily.

Impact #1

Since implementing Sift Payment Protection, Favor Delivery’s chargeback rate has decreased significantly.

Impact #2

Thanks to accurate, automated decisions, the Account Review team is saving significant time in manual review.

Impact #3

With their chargeback rate under control, Favor Delivery has scaled throughout the state of Texas and continues to grow steadily.

Benefit #1	77% Reduction in chargebacks
Benefit #2	3.5x ROI using Sift
Benefit #3	300 Hours in manual review saved monthly

Benefit #1

77% Reduction in chargebacks

Benefit #2

3.5x ROI using Sift

Benefit #3

300 Hours in manual review saved monthly

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Overview

How Favor Delivery achieved growth while reducing risk

Operational Impact

Quantitative Benefit